For many clinical applications the delineation of certain organs or organ parts is a necessary prerequisite. Whenever possible this task will be carried out by automatic unsupervised image processing. This often requires the detection of line structures in an image data set.
One field of application of an image processing device and method for the detection of line structures is the detection of lobar fissures between the lung lobes. Lobar fissures are thin boundaries that divide the lungs into five lobes; the left lung consists of two lobes and the right lung consists of three lobes. Typically, Computed Tomography (CT) is the modality of choice to capture three-dimensional image data of the human lungs. In this context, automatic segmentation of lung lobes from CT data is becoming clinically relevant as an enabler for, e.g., lobe-based quantitative analysis for diagnostics or more accurate interventional planning Attenuation of the fissures in CT scans is typically greater than the surrounding lung parenchyma, so that fissures appear as bright plate-like structures. However, segmentation of the lung lobes is still very challenging especially as the fissures are very thin and thus result in bright lines of only one or two pixel thickness in a cross-sectional view even on latest high resolution CT. For that reason, image noise, partial volume effect, but also different reconstruction kernels and imaging protocols heavily impede the extraction. Finally, lobe segmentation is further complicated once anatomical anomalies are present.
Many efforts have been done in the past decade on lung lobe segmentation from CT data. Most approaches typically build on a similar idea. At first, fissure detection is performed which usually results in a feature image, where the fissures are supposed to be highlighted and other structures are suppressed. This feature image is then integrated into a segmentation algorithm and watersheds, level-sets, graph optimization as well as atlas- and multi-atlas registration have been used for this purpose. As the calculation of the feature image is usually a first step in a more comprehensive segmentation framework, the detection of fissures is crucial and various different methods have been proposed. Zhang et al. (L. Zhang, E. Hoffman, and J. Reinhardt, “Atlas-driven lung lobe segmentation in volumetric X-ray CT images,” IEEE Transactions On Medical Imaging 25(1), pp. 1-16, 2006) and Ukil et al. (S. Ukil and J. Reinhardt, “Anatomy-guided lung lobe segmentation in X-ray CT images”, IEEE Transactions On Medical Imaging 28(2), pp. 202-214, 2009) made use of a 2-D multi-local level set extrinsic-curvature measure (MLSEC) which indeed detects the fissures but also highlights many other structures.
Another often used approach is to analyze the eigenvectors of the Hessian matrix of each voxel to measure if a voxel belongs to a locally plate-like object with bright appearance. From a rich set of features, the ones that are best suited for fissure detection are selected in a supervised learning step. Despite the fact that fissure detection has been addressed by using different approaches, there are still several limitations. Although theoretically the analysis of the eigenvectors of the Hessian matrix is able to detect the bright plate-like fissures, practically, the filter can give low responses for fissure voxels due to the challenges stated above. However, human observers can in most cases still clearly see the fissures even when the filter is not responding well. Obvious segmentation errors that likely happen from this improper detection are thus extremely striking. A comparison of the supervised filter to the Hessian filter showed that the supervised filter results in a better detection. Nevertheless, the main drawback is that this approach requires a large set of ground truth annotations to perform learning. The problem of detecting bright plate-like objects is implicitly solved by learning a combination of a set of low order features.
Thus, although a number of line detection algorithms are known, they fail in the following circumstances: The line to be detected is non-continuous (interrupted, consisting of single points); the line consists of line pieces, which may be slightly curved and are not collinear throughout the image (as assumed e.g. for a Hough-transform); the image is very noisy; or the line is fainter in contrast and intensity than the surrounding image structures.